Resource title

An Artificial Neural Net Attraction Model (ANNAM) to analyze market share effects of marketing instruments

Resource image

image for OpenScout resource :: An Artificial Neural Net Attraction Model (ANNAM) to analyze market share effects of marketing instruments

Resource description

Attraction models are very popular in marketing research for studying the effects of marketing instruments on market shares. However, so far the marketing literature only considers attraction models with certain functional forms that exclude threshold or saturation effects on attraction values. We can achieve greater exibility by using the neural net based approach introduced here. This approach assesses brands' attraction values by means of a perceptron with one hidden layer. The approach uses log-ratio transformed market shares as dependent variables. Stochastic gradient descent followed by a quasi Newton method estimates parameters. For store-level data, neural net models perform better and imply a price response that is qualitatively different from the well-known multinomial logit attraction model. Price elasticities of neural net attraction models also lead to specific managerial implications in terms of optimal prices. (author's abstract) ; Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Resource author

Harald Hruschka

Resource publisher

Resource publish date

Resource language

en

Resource content type

application/pdf

Resource resource URL

http://epub.wu.ac.at/940/1/document.pdf

Resource license

Adapt according to the license agreement. Always reference the original source and author.